{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "from gs_quant.data import Dataset\n",
    "from gs_quant.markets.portfolio import Portfolio\n",
    "from gs_quant.timeseries import returns\n",
    "from gs_quant.risk import MarketDataPattern, MarketDataShock, MarketDataShockBasedScenario, MarketDataShockType\n",
    "from gs_quant.instrument import IRSwap\n",
    "from gs_quant.common import PayReceive\n",
    "from gs_quant import risk\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from gs_quant.datetime.date import business_day_offset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "from gs_quant.session import GsSession\n",
    "# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\n",
    "GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics', 'read_product_data'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we'll look at how several rate and fx instruments moved in the 2012 and 2016 elections and use these moves as shocks for our portfolio.\n",
    "\n",
    "The content of this notebook is split into the following parts:\n",
    "* [1: Visualize last 2 elections](#1:-Visualize-last-2-elections)\n",
    "* [2: Grab portfolio](#2:-Grab-portfolio)\n",
    "* [3: Use prior elections as shocks](#3:-Use-prior-elections-as-shocks)\n",
    "* [4: Custom shocks](#4:-Custom-shocks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1: Visualize last 2 elections\n",
    "\n",
    "Let's start by retrieving the data from the [Marquee data catalog](https://marquee.gs.com/s/discover/data-services/catalog) and visualizing it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "elec_12 = datetime.date(2012, 11, 6)\n",
    "elec_16 = datetime.date(2016, 11, 8)\n",
    "elec_20 = datetime.date(2020, 11, 3)\n",
    "\n",
    "t0 = datetime.date(2012, 1, 1)\n",
    "tn = datetime.date.today()\n",
    "\n",
    "fx = Dataset('FXSPOT_PREMIUM').get_data(t0, tn, bbid='AUDJPY', tenor='3m', fields=('spot',))[['spot']].spot\n",
    "fx_vol = Dataset('FXIMPLIEDVOL_PREMIUM').get_data(t0, tn, bbid='AUDJPY', deltaStrike='25DC', tenor='3m')[['impliedVolatility']].impliedVolatility\n",
    "rates = Dataset('IR_SWAP2').get_data(t0, assetId=['MAAXGV0GZTW4GFNC'], tenor='30y', fields=('rate',))[['rate']].rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def slice_data(df, y_0):\n",
    "    df_period = pd.concat((df.loc[x:y].reset_index() for x, y in\n",
    "                           [(datetime.date(t, 8, 1), datetime.date(t + 1, 1, 31)) for t in [2012, 2016]]), axis=1)\n",
    "    df_period['month'] = pd.DatetimeIndex(df_period.date.iloc[:, 0]).month_name()\n",
    "    df_period.set_index('month', drop=True, inplace=True)\n",
    "    df_period.drop('date', axis=1, inplace=True)\n",
    "    df_period.columns = ['Democrat win 2012', 'Republican win 2016']\n",
    "    return df_period\n",
    "\n",
    "def plot_data(title, data):\n",
    "    ax = data.plot(title=title, figsize=(10, 4))\n",
    "    plt.axvline(x=len(fx[datetime.date(2012, 8, 1):datetime.date(2012, 11, 5)]), linestyle='--', color='r')\n",
    "    ax.set_xticks([21*k for k in range(6)])\n",
    "    _ = ax.set_xticklabels(['August', 'September', 'October', 'November', 'December', 'January'], horizontalalignment='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "fx_sliced, fx_vol_sliced, rates_sliced = [slice_data(x, 2012) for x in [fx, fx_vol, rates]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data('AUDJPY', fx_sliced)\n",
    "plot_data('AUDJPY 3m vol', fx_vol_sliced)\n",
    "plot_data('30y rates', rates_sliced)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2: Grab portfolio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use our FX book from the previous notebook and add a few rates trades. We'll shock this book in the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asset_class</th>\n",
       "      <th>type</th>\n",
       "      <th>buy_sell</th>\n",
       "      <th>call_amount</th>\n",
       "      <th>call_currency</th>\n",
       "      <th>clearing_house</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>expiration_date</th>\n",
       "      <th>expiration_time</th>\n",
       "      <th>fee</th>\n",
       "      <th>...</th>\n",
       "      <th>pay_or_receive</th>\n",
       "      <th>premium</th>\n",
       "      <th>premium_currency</th>\n",
       "      <th>premium_payment_date</th>\n",
       "      <th>principal_exchange</th>\n",
       "      <th>put_amount</th>\n",
       "      <th>put_currency</th>\n",
       "      <th>settlement_date</th>\n",
       "      <th>strike_price</th>\n",
       "      <th>termination_date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>instrument</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&lt;gs_quant.target.instrument.FXOption object at 0x000000001CE38860&gt;</th>\n",
       "      <td>AssetClass.FX</td>\n",
       "      <td>AssetType.Option</td>\n",
       "      <td>BuySell.Sell</td>\n",
       "      <td>-27300000</td>\n",
       "      <td>Currency.USD</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2027-03-24</td>\n",
       "      <td>NYC</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>Currency.USD</td>\n",
       "      <td>2027-03-30</td>\n",
       "      <td></td>\n",
       "      <td>-42000000</td>\n",
       "      <td>Currency.AUD</td>\n",
       "      <td>2027-03-30</td>\n",
       "      <td>0.65</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&lt;gs_quant.target.instrument.IRSwap object at 0x00000000050652E8&gt;</th>\n",
       "      <td>AssetClass.Rates</td>\n",
       "      <td>AssetType.Swap</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>SwapClearingHouse.NONE</td>\n",
       "      <td>2020-09-11</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>PayReceive.Receive</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>PrincipalExchange._None</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2030-09-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&lt;gs_quant.target.instrument.IRSwap object at 0x000000001CE32D68&gt;</th>\n",
       "      <td>AssetClass.Rates</td>\n",
       "      <td>AssetType.Swap</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>SwapClearingHouse.NONE</td>\n",
       "      <td>2020-09-11</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>PayReceive.Pay</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>PrincipalExchange._None</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2025-09-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         asset_class  \\\n",
       "instrument                                                             \n",
       "<gs_quant.target.instrument.FXOption object at ...     AssetClass.FX   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...  AssetClass.Rates   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...  AssetClass.Rates   \n",
       "\n",
       "                                                                type  \\\n",
       "instrument                                                             \n",
       "<gs_quant.target.instrument.FXOption object at ...  AssetType.Option   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...    AssetType.Swap   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...    AssetType.Swap   \n",
       "\n",
       "                                                        buy_sell call_amount  \\\n",
       "instrument                                                                     \n",
       "<gs_quant.target.instrument.FXOption object at ...  BuySell.Sell   -27300000   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...                             \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...                             \n",
       "\n",
       "                                                   call_currency  \\\n",
       "instrument                                                         \n",
       "<gs_quant.target.instrument.FXOption object at ...  Currency.USD   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...                 \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...                 \n",
       "\n",
       "                                                            clearing_house  \\\n",
       "instrument                                                                   \n",
       "<gs_quant.target.instrument.FXOption object at ...                           \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...  SwapClearingHouse.NONE   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...  SwapClearingHouse.NONE   \n",
       "\n",
       "                                                   effective_date  \\\n",
       "instrument                                                          \n",
       "<gs_quant.target.instrument.FXOption object at ...                  \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...     2020-09-11   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...     2020-09-11   \n",
       "\n",
       "                                                   expiration_date  \\\n",
       "instrument                                                           \n",
       "<gs_quant.target.instrument.FXOption object at ...      2027-03-24   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...                   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...                   \n",
       "\n",
       "                                                   expiration_time fee  ...  \\\n",
       "instrument                                                              ...   \n",
       "<gs_quant.target.instrument.FXOption object at ...             NYC      ...   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...                   0  ...   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...                   0  ...   \n",
       "\n",
       "                                                        pay_or_receive  \\\n",
       "instrument                                                               \n",
       "<gs_quant.target.instrument.FXOption object at ...                       \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...  PayReceive.Receive   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...      PayReceive.Pay   \n",
       "\n",
       "                                                   premium premium_currency  \\\n",
       "instrument                                                                    \n",
       "<gs_quant.target.instrument.FXOption object at ...       0     Currency.USD   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...                            \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...                            \n",
       "\n",
       "                                                   premium_payment_date  \\\n",
       "instrument                                                                \n",
       "<gs_quant.target.instrument.FXOption object at ...           2027-03-30   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...                        \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...                        \n",
       "\n",
       "                                                         principal_exchange  \\\n",
       "instrument                                                                    \n",
       "<gs_quant.target.instrument.FXOption object at ...                            \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...  PrincipalExchange._None   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...  PrincipalExchange._None   \n",
       "\n",
       "                                                   put_amount  put_currency  \\\n",
       "instrument                                                                    \n",
       "<gs_quant.target.instrument.FXOption object at ...  -42000000  Currency.AUD   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...                            \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...                            \n",
       "\n",
       "                                                   settlement_date  \\\n",
       "instrument                                                           \n",
       "<gs_quant.target.instrument.FXOption object at ...      2027-03-30   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...                   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...                   \n",
       "\n",
       "                                                   strike_price  \\\n",
       "instrument                                                        \n",
       "<gs_quant.target.instrument.FXOption object at ...         0.65   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...                \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...                \n",
       "\n",
       "                                                   termination_date  \n",
       "instrument                                                           \n",
       "<gs_quant.target.instrument.FXOption object at ...                   \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...       2030-09-11  \n",
       "<gs_quant.target.instrument.IRSwap object at 0x...       2025-09-11  \n",
       "\n",
       "[3 rows x 37 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mappers = {\n",
    "    'pair': lambda row: row['foreign ccy'] + 'USD',\n",
    "    'notional_amount': 'notional',\n",
    "    'expiration_date': 'expiry',\n",
    "    'option_type': lambda row: 'Call' if row['C/P'] == 'C' else 'Put',\n",
    "    'strike_price': 'strike',\n",
    "    'premium': lambda row: 0\n",
    "}\n",
    "\n",
    "portfolio = Portfolio.from_csv(r'FXBook.csv', mappings=mappers)\n",
    "\n",
    "swap_10y = IRSwap(PayReceive.Receive, '10y', 'USD', notional_amount= 250e6, fixed_rate='atm+25')  \n",
    "swap_5y = IRSwap(PayReceive.Pay, '5y', 'USD', notional_amount= 500e6, fixed_rate='atm+20') \n",
    "\n",
    "portfolio.append((swap_10y, swap_5y))\n",
    "portfolio.resolve()\n",
    "frame = portfolio.to_frame()\n",
    "frame.index = frame.index.droplevel(0)\n",
    "frame.replace(np.nan,'',True) \n",
    "frame.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3: Use prior market moves as shocks\n",
    "\n",
    "Let's now use the moves we examined in the first section as shocks to the portfolio in the second section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>spot</th>\n",
       "      <th>impliedVolatility</th>\n",
       "      <th>rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012 Election</th>\n",
       "      <td>0.1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 Election</th>\n",
       "      <td>2.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>25.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               spot  impliedVolatility  rate\n",
       "2012 Election   0.1               -1.1  -6.2\n",
       "2016 Election   2.3                6.0  25.9"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_window = 2 \n",
    "effective12 = business_day_offset(elec_12, time_window) \n",
    "effective16 = business_day_offset(elec_16, time_window)  \n",
    "\n",
    "fx_shocks = fx.pct_change(time_window+5).loc[[effective12, effective16]] \n",
    "fx_vol_shocks = fx_vol.pct_change(time_window+5).loc[[effective12, effective16]] \n",
    "ir_shocks = rates.diff(time_window+5).loc[[effective12, effective16]] \n",
    "\n",
    "shock_df = pd.concat([fx_shocks*1e2, fx_vol_shocks*1e2, ir_shocks*1e4], axis=1).round(1) \n",
    "shock_df.index = ['2012 Election', '2016 Election'] \n",
    "\n",
    "def get_market_scenario(s_date): \n",
    "    return MarketDataShockBasedScenario(shocks={ \n",
    "        MarketDataPattern('IR', 'USD'): MarketDataShock(MarketDataShockType.Absolute, ir_shocks.loc[s_date]), \n",
    "        MarketDataPattern('FX', 'USD/AUD'): MarketDataShock(MarketDataShockType.Proportional, fx_shocks.loc[s_date]), \n",
    "        MarketDataPattern('FX', 'JPY/USD'): MarketDataShock(MarketDataShockType.Proportional, 0), \n",
    "        MarketDataPattern('FX Vol', 'JPY/AUD', 'ATM Vol'): MarketDataShock(MarketDataShockType.Absolute, fx_vol_shocks.loc[s_date]),\n",
    "        MarketDataPattern('FX Vol', 'USD/AUD', 'ATM Vol'): MarketDataShock(MarketDataShockType.Absolute, fx_vol_shocks.loc[s_date]),\n",
    "        MarketDataPattern('FX Vol', 'JPY/USD', 'ATM Vol'): MarketDataShock(MarketDataShockType.Absolute, fx_vol_shocks.loc[s_date]),\n",
    "\n",
    "    }) \n",
    "\n",
    "win_12 = get_market_scenario(effective12) \n",
    "win_16 = get_market_scenario(effective16) \n",
    "shock_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 2 artists>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8TnuvTYBrk3wjyWeSvOQ0LveMZfDXsFGfMTDCedYDtwCfqKojXa1Pgk5ep+uZ/5Pnv1XVhcDXgY91uMRmGPw1aoFnDHy391wCTvZ8giGmgIer6uPdr1Qt6+h1+j3gKeDvetufAS48Dcs94xn8Negkzxg45fMJhpzrj4EXAB/oep1qW1ev05r/dOjngYt7u94M3N/pYhvhJ23XoCS/BnwN+BbwTG/3HzF/f/RvgHOBx4C3V9WTvZ95FHg+cBbwfeCtwA+Bo8C3gf/pnef6qrppWX4RndG6ep1W1f1JXgb8FXA2MAdcVlWPLd9vc2Yw+JLUCG/pSFIjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1Ij/heHKCt7Sk0Z5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scenario carrying swap to election day and then applying spot shock\n",
    "results, results_agg = {}, {}\n",
    "\n",
    "with win_12:\n",
    "    results['2012'] = portfolio.dollar_price()\n",
    "\n",
    "with win_16:\n",
    "    results['2016'] = portfolio.dollar_price()\n",
    "\n",
    "results['eod'] = portfolio.dollar_price()\n",
    "\n",
    "for scenario in ['2012', '2016']:\n",
    "    results_agg[scenario] = results[scenario].aggregate() - results['eod'].aggregate()\n",
    "\n",
    "plt.bar(results_agg.keys(), results_agg.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4: Custom shocks\n",
    "\n",
    "Finally let's look at how to run a completely custom shock."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mkt_type</th>\n",
       "      <th>mkt_asset</th>\n",
       "      <th>mkt_class</th>\n",
       "      <th>mkt_point</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>IR</td>\n",
       "      <td>AUD</td>\n",
       "      <td>SWAP</td>\n",
       "      <td>5Y</td>\n",
       "      <td>-31481.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>IR</td>\n",
       "      <td>USD</td>\n",
       "      <td>SWAP</td>\n",
       "      <td>5Y</td>\n",
       "      <td>275314.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>IR</td>\n",
       "      <td>USD</td>\n",
       "      <td>SWAP</td>\n",
       "      <td>10Y</td>\n",
       "      <td>-249668.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mkt_type mkt_asset mkt_class mkt_point     value\n",
       "25       IR       AUD      SWAP        5Y  -31481.0\n",
       "54       IR       USD      SWAP        5Y  275314.0\n",
       "59       IR       USD      SWAP       10Y -249668.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get points available to shock by looking at irdelta \n",
    "threshold = 25000\n",
    "delta = portfolio.calc(risk.IRDelta)\n",
    "delta.aggregate()[(delta.aggregate().value>threshold) | (delta.aggregate().value<-threshold)].round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mkt_type</th>\n",
       "      <th>mkt_asset</th>\n",
       "      <th>mkt_class</th>\n",
       "      <th>mkt_point</th>\n",
       "      <th>shock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IR</td>\n",
       "      <td>AUD</td>\n",
       "      <td>SWAP</td>\n",
       "      <td>5Y</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IR</td>\n",
       "      <td>USD</td>\n",
       "      <td>SWAP</td>\n",
       "      <td>10Y</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IR</td>\n",
       "      <td>USD</td>\n",
       "      <td>SWAP</td>\n",
       "      <td>5Y</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  mkt_type mkt_asset mkt_class mkt_point  shock\n",
       "0       IR       AUD      SWAP        5Y    NaN\n",
       "1       IR       USD      SWAP       10Y    NaN\n",
       "2       IR       USD      SWAP        5Y    NaN"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custom_shocks = pd.read_csv(r'curve_shocks.csv', na_values='')\n",
    "custom_shocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mkt_type</th>\n",
       "      <th>mkt_asset</th>\n",
       "      <th>mkt_class</th>\n",
       "      <th>mkt_point</th>\n",
       "      <th>shock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IR</td>\n",
       "      <td>AUD</td>\n",
       "      <td>SWAP</td>\n",
       "      <td>5Y</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IR</td>\n",
       "      <td>USD</td>\n",
       "      <td>SWAP</td>\n",
       "      <td>10Y</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IR</td>\n",
       "      <td>USD</td>\n",
       "      <td>SWAP</td>\n",
       "      <td>5Y</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  mkt_type mkt_asset mkt_class mkt_point  shock\n",
       "0       IR       AUD      SWAP        5Y     50\n",
       "1       IR       USD      SWAP       10Y     40\n",
       "2       IR       USD      SWAP        5Y     30"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill in your shocks programatically or in excel\n",
    "custom_shocks['shock'] = [50, 40, 30]\n",
    "custom_shocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 3 artists>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shocks = {\n",
    "    MarketDataPattern(custom_shocks.at[i, 'mkt_type'], custom_shocks.at[i, 'mkt_asset'], custom_shocks.at[i, 'mkt_class']):\n",
    "    MarketDataShock(MarketDataShockType.Absolute, custom_shocks.at[i, 'shock'] / 1e4) for i in range(len(custom_shocks)-2)\n",
    "}\n",
    "\n",
    "shocks.update({\n",
    "    MarketDataPattern(custom_shocks.at[i, 'mkt_type'], custom_shocks.at[i, 'mkt_asset'], custom_shocks.at[i, 'mkt_class'],\n",
    "                      (custom_shocks.at[i, 'mkt_point'],)): \n",
    "    MarketDataShock(MarketDataShockType.Absolute, custom_shocks.at[i, 'shock'] / 1e4) for i in [len(custom_shocks)-2, len(custom_shocks)-1]\n",
    "})\n",
    "\n",
    "with MarketDataShockBasedScenario(shocks):\n",
    "    results_agg['custom shocks'] = portfolio.dollar_price().aggregate() - results['eod'].aggregate()\n",
    "\n",
    "plt.bar(results_agg.keys(), results_agg.values())"
   ]
  },
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    "### Disclaimers\n",
    "\n",
    "Scenarios/predictions: Simulated results are for illustrative purposes only. GS provides no assurance or guarantee that the strategy will operate or would have operated in the past in a manner consistent with the above analysis. Past performance figures are not a reliable indicator of future results.\n",
    "\n",
    "Indicative Terms/Pricing Levels: This material may contain indicative terms only, including but not limited to pricing levels. There is no representation that any transaction can or could have been effected at such terms or prices. Proposed terms and conditions are for discussion purposes only. Finalized terms and conditions are subject to further discussion and negotiation.\n",
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    "\n",
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    "\n",
    "OTC Derivatives Risk Disclosures: \n",
    "Terms of the Transaction: To understand clearly the terms and conditions of any OTC derivative transaction you may enter into, you should carefully review the Master Agreement, including any related schedules, credit support documents, addenda and exhibits. You should not enter into OTC derivative transactions unless you understand the terms of the transaction you are entering into as well as the nature and extent of your risk exposure. You should also be satisfied that the OTC derivative transaction is appropriate for you in light of your circumstances and financial condition. You may be requested to post margin or collateral to support written OTC derivatives at levels consistent with the internal policies of Goldman Sachs. \n",
    " \n",
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    " \n",
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    " \n",
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    " \n",
    "Pricing and Valuation: The price of each OTC derivative transaction is individually negotiated between Goldman Sachs and each counterparty and Goldman Sachs does not represent or warrant that the prices for which it offers OTC derivative transactions are the best prices available, possibly making it difficult for you to establish what is a fair price for a particular OTC derivative transaction; The value or quoted price of the Product at any time, however, will reflect many factors and cannot be predicted. If Goldman Sachs makes a market in the offered Product, the price quoted by Goldman Sachs would reflect any changes in market conditions and other relevant factors, and the quoted price (and the value of the Product that Goldman Sachs will use for account statements or otherwise) could be higher or lower than the original price, and may be higher or lower than the value of the Product as determined by reference to pricing models used by Goldman Sachs. If at any time a third party dealer quotes a price to purchase the Product or otherwise values the Product, that price may be significantly different (higher or lower) than any price quoted by Goldman Sachs. Furthermore, if you sell the Product, you will likely be charged a commission for secondary market transactions, or the price will likely reflect a dealer discount. Goldman Sachs may conduct market making activities in the Product. To the extent Goldman Sachs makes a market, any price quoted for the OTC derivative transactions, Goldman Sachs may differ significantly from (i) their value determined by reference to Goldman Sachs pricing models and (ii) any price quoted by a third party. The market price of the OTC derivative transaction may be influenced by many unpredictable factors, including economic conditions, the creditworthiness of Goldman Sachs, the value of any underlyers, and certain actions taken by Goldman Sachs. \n",
    " \n",
    "Market Making, Investing and Lending: Goldman Sachs engages in market making, investing and lending businesses for its own account and the accounts of its affiliates in the same or similar instruments underlying OTC derivative transactions (including such trading as Goldman Sachs deems appropriate in its sole discretion to hedge its market risk in any OTC derivative transaction whether between Goldman Sachs and you or with third parties) and such trading may affect the value of an OTC derivative transaction. \n",
    " \n",
    "Early Termination Payments: The provisions of an OTC Derivative Transaction may allow for early termination and, in such cases, either you or Goldman Sachs may be required to make a potentially significant termination payment depending upon whether the OTC Derivative Transaction is in-the-money to Goldman Sachs or you at the time of termination. Indexes: Goldman Sachs does not warrant, and takes no responsibility for, the structure, method of computation or publication of any currency exchange rates, interest rates, indexes of such rates, or credit, equity or other indexes, unless Goldman Sachs specifically advises you otherwise.\n",
    "Risk Disclosure Regarding futures, options, equity swaps, and other derivatives as well as non-investment-grade securities and ADRs: Please ensure that you have read and understood the current options, futures and security futures disclosure document before entering into any such transactions. Current United States listed options, futures and security futures disclosure documents are available from our sales representatives or at http://www.theocc.com/components/docs/riskstoc.pdf,  http://www.goldmansachs.com/disclosures/risk-disclosure-for-futures.pdf and https://www.nfa.futures.org/investors/investor-resources/files/security-futures-disclosure.pdf, respectively. Certain transactions - including those involving futures, options, equity swaps, and other derivatives as well as non-investment-grade securities - give rise to substantial risk and are not available to nor suitable for all investors. If you have any questions about whether you are eligible to enter into these transactions with Goldman Sachs, please contact your sales representative. Foreign-currency-denominated securities are subject to fluctuations in exchange rates that could have an adverse effect on the value or price of, or income derived from, the investment. In addition, investors in securities such as ADRs, the values of which are influenced by foreign currencies, effectively assume currency risk.\n",
    "Options Risk Disclosures: Options may trade at a value other than that which may be inferred from the current levels of interest rates, dividends (if applicable) and the underlier due to other factors including, but not limited to, expectations of future levels of interest rates, future levels of dividends and the volatility of the underlier at any time prior to maturity. Note: Options involve risk and are not suitable for all investors. Please ensure that you have read and understood the current options disclosure document before entering into any standardized options transactions. United States listed options disclosure documents are available from our sales representatives or at http://theocc.com/publications/risks/riskstoc.pdf. A secondary market may not be available for all options. Transaction costs may be a significant factor in option strategies calling for multiple purchases and sales of options, such as spreads. When purchasing long options an investor may lose their entire investment and when selling uncovered options the risk is potentially unlimited. Supporting documentation for any comparisons, recommendations, statistics, technical data, or other similar information will be supplied upon request.\n",
    "This material is for the private information of the recipient only. This material is not sponsored, endorsed, sold or promoted by any sponsor or provider of an index referred herein (each, an \"Index Provider\"). GS does not have any affiliation with or control over the Index Providers or any control over the computation, composition or dissemination of the indices. While GS will obtain information from publicly available sources it believes reliable, it will not independently verify this information. Accordingly, GS shall have no liability, contingent or otherwise, to the user or to third parties, for the quality, accuracy, timeliness, continued availability or completeness of the data nor for any special, indirect, incidental or consequential damages which may be incurred or experienced because of the use of the data made available herein, even if GS has been advised of the possibility of such damages.\n",
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    "The offer of any securities mentioned in this message may not be made to the general public in Brazil. Accordingly, any such securities have not been nor will they be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários) nor has any offer been submitted to the foregoing agency for approval. Documents relating to the offer, as well as the information contained therein, may not be supplied to the public in Brazil, as the offer is not a public offering of securities in Brazil. These terms will apply on every access to Marquee.\n",
    "Ouvidoria Goldman Sachs Brasil: 0800 727 5764 e/ou ouvidoriagoldmansachs@gs.com\n",
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    "Ombudsman Goldman Sachs Brazil: 0800 727 5764 and / or ouvidoriagoldmansachs@gs.com\n",
    "Available Weekdays (except holidays), from 9 am to 6 pm.\n",
    "\n"
   ]
  }
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